Forced Intracellular Degradation of Xenoantigens: A Novel Modality for Cell-Based Cancer Immunotherapy
Bibliographic record
Abstract
Abstract Background: Antigen presentation is crucial in fighting cancer and infectious diseases. This process is however dependent on proteasomal degradation of captured or intracellular antigens. Given the recent leverage of mesenchymal stromal cells (MSCs) as a potent vaccination platform, we investigated whether forced degradation of an expressed experimental antigen fused to small degron sequences could prime potent antitumoral responses. Methods: Retroviral vectors were used to gene-engineer murine MSCs. Besides comparing their in vitro antigen presentation capacity, qPCR and flow-cytometry were used to quantify gene expression and assess reactive oxygen (ROS) species production respectively. In addition, an immunopeptidome analysis was conducted to mine the peptide repertoire of engineered cells. Finally, the therapeutic potency of engineered MSCs was evaluated as a monotherapy or in combination with anti-PD-1 in syngeneic immunocompetent mice with a pre-established solid T-cell lymphoma. Results: Despite similar transgene expression and absent changes in the expression of genes related to antigen presentation/loading or endoplasmic-reticulum stress, the MSC-UBvR-OVA group triggered potent T-cell activation due to enriched cell surface presentation of OVA-derived peptides. Nevertheless, the UBvR degron elicits mitochondrial ROS production, which appears to be important for efficient antigen processing. Administration of MSC-UBvR-OVA as a monotherapy to cancer-bearing mice controls tumor growth, an effect further enhanced using tranylcypromine-stimulated MSCs combined with the anti-PD-1 immune checkpoint. Conclusions: Forced antigen degradation using the UBvR degron represents a plausible modality for the future development of MSC-based cancer vaccine expressing relevant tumor antigens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".